Multi-Objective Reactive Power Optimization by Fuzzy Cluster and Learning Automata

To perform reactive power compensation,it is necessary to select the site where the compensator is configured and the needed compensation capacity.The weak nodes in power system are searched by fuzzy cluster and the information of candidate nodes is obtained;during the dynamic fuzzy cluster,the indices of U/U0,? and voltage deviation are applied.Synthetically considering the minimum generation cost and reactive compensation cost,the minimum voltage deviation and minimum active network loss,a multi-objective optimization model for candidate nodes,where reactive compensator may be configured,is built and the optimal trade-off solution of the optimization problem is obtained by learning automata.Applying fuzzy cluster and learning automata to IEEE 57-bus system,calculation results show that the proposed method is effective.